Analysing the impact of

population,Environment

and Health

(Team 5)

Team Members:

•Jyothirmai Vallakati - 11697705

•Manohar Varma Buddharaju -

11724300

•Keerthi Yanala - 11629948

•Pranay Rajana - 11667766

Introduction

Domain:

•Analyzing of Population, Environment, and Health

•This presentation focuses on the analysis of key factors impacting population

dynamics, environmental sustainability, and public health.

•Our goal is to explore the interplay between population trends, environmental factors

such as CO2 emissions, and indicators of public health such as life expectancy.

•By understanding these relationships, we aim to provide insights that can inform

policy decisions, interventions, and strategies aimed at promoting sustainable

development and improving public health outcomes.

Data Abstraction

Dataset:

Type: Three structured datasets CO2 emissions, population statistics, life expectancy data.

Attributes: Each dataset contains attributes such as year, country, CO2 emissions (metric tons per capita), population,

life expectancy, GDP (in USD), etc.

Number of Records:

The number of records varies depending on the dataset.

The CO2 emissions dataset contain annual records for multiple countries spanning several decades, while the

population dataset include population figures for each country over time.

Data Transformation:

Aggregation: Aggregating CO2 emissions data into 5-year intervals to smooth trends.

Binning: Binning urban population data to analyze its impact on life expectancy.

Above Workflow Explaination

Data Collection:

•Getting relevant datasets from Kaggle, a popular platform for data science datasets.

Initial Visualization:

•Utilizing D3.js to visualize the uncleaned dataset, providing an initial overview of the data's structure and potential insights.

Data Cleaning:

•Using Python programming language and libraries like Pandas and NumPy to clean the dataset, handling missing values, outliers,

and inconsistencies.

Refined Visualization:

•Using Python's data visualization libraries such as Matplotlib or Seaborn to create more refined visualizations based on the

cleaned dataset, focusing on specific variables of interest.

Dashboard Creation:

•Utilizing Microsoft Power BI to design interactive dashboards and reports, incorporating the refined visualizations to provide a

comprehensive view of the analyzed data.

Report Generation:

•Creating detailed reports summarizing the analysis findings, insights, and conclusions drawn from the visualizations and data

analysis process.

Task Abstraction

Task:

Target:

•Analyzinf the impact of population, environment, and health factors.

Actions:

•Select relevant datasets: Choose datasets containing CO2 emissions, population statistics, life expectancy data,

GDP figures.

•Design visualizations: Planning and creating visualizations such as line charts, histograms, scatter plots, and box

plots to explore relationships between variables.

•Implement visualizations: Developing visualizations using tools like D3.js, Python, and Microsoft Power BI.

•Analyze insights: Interpreting visualizations to identify trends, correlations, and patterns in the data.

•Draw conclusions: Using insights from visualizations to draw conclusions about the impact of population,

environment, and health factors on global and regional trends.

Implementation using Tools

Tools Used:

D3.js:

•Description: D3.js (Data-Driven Documents) is a JavaScript library commonly used for creating dynamic, interactive data

visualizations in web browsers. It offers powerful capabilities for manipulating HTML, SVG, and CSS to generate custom

visualizations.

•Purpose: Initial visualization of uncleaned dataset, providing interactive charts to explore the data's structure.

Python:

•Description: Python is a versatile programming language with a rich ecosystem of libraries for data manipulation, analysis, and

visualization. Libraries such as Pandas, NumPy, Matplotlib, and Seaborn are commonly used for data processing and visualization

tasks.

•Purpose: Data cleaning, refining visualizations, and creating more sophisticated charts and plots based on the cleaned dataset.

Microsoft Power BI:

•Description: Microsoft Power BI is a business analytics tool that enables users to visualize and analyze data, create interactive

reports, and share insights across their organization. It offers a range of features for data visualization, data modeling, and

dashboard creation.

•Purpose: Designing interactive dashboards and reports, incorporating visualizations to provide a comprehensive view of the

analyzed data

Results for Analysis

CO2 Emissions

Using D3.js

•Visualization: Line chart showing CO2

emissions over the years.

•Explanation: This visualization presents an

overview of global CO2 emissions trends over

time. It helps viewers understand how CO2

emissions have changed globally over the

years.

•Storytelling: "Over the past few decades,

global CO2 emissions have shown varying

trends. From the early years, where emissions

were relatively low, to the recent years where

emissions have surged, this chart reveals the

significant impact of human activities on

carbon emissions."

CO2 Emissions Distribution:

•Visualization: Histogram showing the distribution

of CO2 emissions across different emission levels.

•Explanation: This histogram highlights the

distribution pattern of CO2 emissions. It helps in

understanding the frequency of emissions falling

within different ranges.

•Storytelling: "Understanding the distribution of

CO2 emissions is crucial for addressing climate

change. By examining this histogram, we can

identify the most common emission levels and

assess the need for targeted emission reduction

strategies."

Top 10 Countries by Population:

•Visualization: Bar chart showing the top 10

countries by population.

•Explanation: This visualization ranks countries

based on their population size, allowing

viewers to compare the population of different

countries visually.

•Storytelling: "Population size varies

significantly across countries. From populous

nations like China and India to smaller

countries like Japan and Russia, this bar chart

illustrates the diversity in population

distribution around the world."

Least 10 Countries by Population:

•Visualization: Bar chart showing the least 10

countries by population.

•Explanation: This visualization highlights the

countries with the smallest populations,

providing insights into less populated regions.

•Storytelling: "While some countries boast large

populations, others have relatively small

populations. Exploring the least 10 countries

by population reveals insights into regions with

lower population densities and their unique

demographic challenges."

Visualisation Using Python While filtering

Data:

•Visualization :Line chart showing the average CO2 emissions over 5-year

intervals.

•Explanation :This visualization aggregates the CO2 emissions data into 5-

year intervals and calculates the average emissions for each interval. It

provides a smoothed overview of CO2 emission trends, reducing noise

and fluctuations observed in annual data.

•Storytelling : "Analyzing CO2 emissions on an annual basis can

sometimes obscure long-term trends due to yearly fluctuations. To

address this, we aggregate the data into 5-year intervals, providing a

more stable view of CO2 emission trends over time. This line chart

illustrates how average CO2 emissions have evolved over these

intervals, offering insights into the broader patterns of carbon emissions

and their impacts on climate change. From the early years with

relatively low emissions to the recent decades where emissions have

soared, this visualization helps us understand the trajectory of CO2

emissions and the urgency of addressing environmental challenges."

The distribution of life expectancy

•Visualization: Histogram showing the distribution of life expectancy.

•Explanation: This visualization provides insights into the distribution of

life expectancy across a population. It segments life expectancy values

into bins and counts the frequency of values falling within each bin. By

visualizing the distribution, we can understand the typical range of life

expectancies and any potential patterns or outliers.

•Storytelling: "Life expectancy is a critical indicator of overall population

health and well-being. By examining the distribution of life expectancy

values through this histogram, we gain valuable insights into the

demographic characteristics of a population. The shape of the histogram

reveals the typical range of life expectancies within the population, with

peaks indicating common life spans and tails representing outliers.

Understanding the distribution of life expectancy is essential for

policymakers, healthcare professionals, and researchers to address

disparities in healthcare access, improve public health interventions, and

enhance overall quality of life."

The scatter plot of population vs. life

expectancy:

•Visualization: Scatter plot showing the relationship between population

and life expectancy.

•Explanation: This visualization explores the relationship between

population size and life expectancy across different regions or countries.

Each point on the scatter plot represents a specific region or country,

with its position determined by its population size on the x-axis and its

life expectancy on the y-axis. By plotting these two variables together,

we can identify any potential correlations or patterns between

population size and life expectancy.

•Storytelling: "Population size and life expectancy are two key indicators

of a region's or country's overall well-being and development. This

scatter plot allows us to explore the relationship between these two

variables. As we observe the distribution of points, we may notice

certain trends emerging. For instance, regions with larger populations

may exhibit a wide range of life expectancies, indicating disparities in

healthcare access and socio-economic factors. Conversely, regions with

smaller populations may show more homogeneous life expectancy

values. Understanding the relationship between population size and life

expectancy is crucial for policymakers and healthcare professionals to

implement targeted interventions aimed at improving public health

outcomes and enhancing quality of life."

Co2 emissions vs. life expectancy:

•Visualization: Scatter plot showing the relationship between CO2 emissions and

life expectancy.

•Explanation: This visualization examines the relationship between CO2

emissions and life expectancy across different countries or regions. Each point

on the scatter plot represents a specific country and year, with its position

determined by its CO2 emissions on the x-axis and its life expectancy on the y-

axis. By plotting these two variables together, we can explore whether there is

any association between carbon emissions and life expectancy.

•Storytelling: "The correlation between CO2 emissions and life expectancy is a

topic of significant interest in the context of environmental sustainability and

public health. This scatter plot allows us to visualize the relationship between

these two variables. As we examine the distribution of points, we may discern

certain trends or patterns. Countries with higher CO2 emissions may exhibit

varying levels of life expectancy, indicating potential impacts of environmental

pollution on public health outcomes. Conversely, countries with lower CO2

emissions may demonstrate relatively higher life expectancies. Exploring this

relationship can inform policymakers and stakeholders about the potential

health implications of carbon emissions and the importance of implementing

sustainable practices to safeguard public health and well-being."

life expectancy vs. GDP:

•Visualization: Scatter plot showing the relationship between GDP (Gross

Domestic Product) and life expectancy.

•Explanation: This visualization explores the relationship between a country's

GDP and life expectancy. Each point on the scatter plot represents a specific

country, with its position determined by its GDP on the x-axis (using a

logarithmic scale) and its life expectancy on the y-axis. By plotting these two

variables together, we can examine whether there is any correlation between a

country's economic prosperity (as measured by GDP) and the life expectancy of

its population.

•Storytelling: "The relationship between a country's economic status and the

health outcomes of its population is a topic of considerable interest. This

scatter plot visualizes the association between GDP and life expectancy across

different countries. As we observe the distribution of points, we may notice

certain trends emerging. Countries with higher GDPs may exhibit longer life

expectancies, reflecting the potential benefits of economic prosperity on

healthcare access, infrastructure development, and overall quality of life.

Conversely, countries with lower GDPs may demonstrate shorter life

expectancies, highlighting the challenges faced by less economically developed

regions in providing adequate healthcare and social services. Exploring this

relationship can provide valuable insights into the complex interplay between

economic factors and public health outcomes."

pairplot:

•Explanation: The pairplot is a grid of scatterplots and

histograms showing the relationships between pairs of

variables in the dataset. Each cell in the grid represents the

interaction between two variables. The diagonal cells display

histograms of individual variables, while the off-diagonal cells

display scatterplots showing the relationship between pairs

of variables.

•Storytelling: "The pairplot provides a comprehensive

overview of the relationships between various factors and life

expectancy. By examining the scatterplots and histograms in

the pairplot, we can identify potential correlations, trends,

and distributions among the variables. For example, we can

explore how population size, GDP, agricultural land

percentage, forested area percentage, infant mortality rate,

urban population percentage, and out-of-pocket health

expenditure relate to life expectancy. This visualization allows

us to identify patterns and associations that may inform

further analysis and policymaking efforts aimed at improving

public health outcomes and overall well-being."

The heatmap of the correlation matrix:

•Explanation: The heatmap of the correlation matrix visualizes the

pairwise correlations between numeric variables in the dataset. Each

cell in the heatmap represents the correlation coefficient between two

variables. The color intensity of each cell indicates the strength and

direction of the correlation: warmer colors (e.g., red) indicate positive

correlations, while cooler colors (e.g., blue) indicate negative

correlations.

•Storytelling: "The heatmap of the correlation matrix offers valuable

insights into the relationships between different factors in our dataset.

By examining the color gradients in the heatmap, we can discern which

variables are positively or negatively correlated with each other. Strong

positive correlations are depicted by warmer colors, suggesting that

changes in one variable are associated with corresponding changes in

another variable. Conversely, strong negative correlations are depicted

by cooler colors, indicating an inverse relationship between variables.

This visualization aids in identifying potential multicollinearity among

variables and can guide further analysis and modeling efforts to better

understand the underlying patterns and drivers of life expectancy."

The boxplot of life expectancy by urban

population bins:

•Explanation: The boxplot visualizes the distribution of life expectancy

across different categories of urban population. The

'Urban_population_bins' column is created by binning the

'Urban_population' column into categories, and the boxplot shows the

distribution of life expectancy within each bin. The boxplot displays the

median, quartiles, and potential outliers in the data.

•Storytelling: "The boxplot provides insights into how life expectancy

varies across different levels of urban population. By categorizing urban

population into bins, we can observe the distribution of life expectancy

within each category. The horizontal line within each box represents the

median life expectancy, while the box itself spans the interquartile range

(IQR), indicating the middle 50% of the data. The whiskers extend to 1.5

times the IQR from the quartiles, and any points beyond the whiskers

are considered potential outliers. This visualization allows us to compare

the central tendency and spread of life expectancy across different

levels of urban population, helping us understand how urbanization may

impact public health outcomes."

The histogram of GDP distribution:

•Explanation: The histogram visualizes the distribution of GDP

(Gross Domestic Product) values. Each bar in the histogram

represents a range of GDP values, and the height of the bar

indicates the frequency or count of observations falling

within that range. The histogram provides insights into the

distribution pattern and variability of GDP across the dataset.

•Storytelling: "The histogram offers a detailed view of the

distribution of GDP values across the dataset. By examining

the shape and spread of the histogram, we can gain insights

into the distribution pattern and variability of economic

prosperity represented by GDP. In this visualization, the

presence of a KDE (Kernel Density Estimation) curve provides

additional information about the density of GDP values,

allowing us to identify potential peaks, modes, and outliers

in the data distribution. Understanding the distribution of

GDP is essential for assessing economic inequality,

identifying regions of prosperity or stagnation, and informing

policy decisions aimed at promoting sustainable economic

growth and development."

The scatterplot matrix:

•Explanation: The scatterplot matrix, generated using the

pairplot function, provides a visual overview of the

relationships between multiple variables in the dataset. Each

cell in the matrix contains a scatter plot showing the

relationship between two variables. The diagonal cells

display histograms of individual variables.

•Storytelling: "The scatterplot matrix offers a comprehensive

view of the relationships between various factors and life

expectancy. By examining the scatter plots and histograms in

the matrix, we can identify potential correlations, trends,

and distributions among the variables. For instance, we can

explore how population size, GDP, agricultural land

percentage, forested area percentage, infant mortality rate,

urban population percentage, and out-of-pocket health

expenditure relate to life expectancy. This visualization

allows us to identify patterns and associations that may

inform further analysis and policymaking efforts aimed at

improving public health outcomes and overall well-being."

Next we are going to create more

visual insights using powerBI by

embedding report here.

Microsoft Power BI

Work Management: Implementation Status Report

Work Completed:

Data Collection:

•Datasets retrieved from Kaggle.

•Data sources identified and downloaded.

•Initial exploration of datasets completed.

Visualization Development:

•Initial visualizations created using D3.js for uncleaned dataset.

•Visualizations refined and enhanced using Python libraries (Matplotlib, Seaborn) for cleaned dataset.

•Dynamic dashboards and reports created using Microsoft Power BI.

Report Generation:

•Detailed reports summarizing analysis findings, insights, and conclusions drawn from visualizations and data analysis process.

•Reports formatted and finalized for presentation.

Responsibilities and Contributions:

Jyothirmai Vallakati (11697705): 25%

•Data Collection

•Visualization Development (Python)

Manohar Varma Buddharaju (11724300): 25%

•Data Collection

•Visualization Development (D3.js)

Keerthi Yanala (11629948): 25%

•Visualization Development (Python)

•Report Generation(powerbi)

Pranay Rajana (11667766): 25%

•Data Collection

•Report Generation(powerbi)

References/Bibliography

1. Kaggle: https://www.kaggle.com - Source of datasets used in the analysis.

2. D3.js Documentation: https://d3js.org - Documentation for D3.js library.

3. Python Documentation: https://www.python.org/doc - Official documentation for the Python programming language.

4. Pandas Documentation: https://pandas.pydata.org/docs - Documentation for Pandas library.

5. NumPy Documentation: https://numpy.org/doc - Documentation for NumPy library.

6. Matplotlib Documentation: https://matplotlib.org/stable/contents.html - Documentation for Matplotlib library.

7. Seaborn Documentation: https://seaborn.pydata.org - Documentation for Seaborn library.

8. Microsoft Power BI Documentation: https://docs.microsoft.com/en-us/power-bi/ - Documentation for Microsoft Power

BI.

Thank You